Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

Despite having achieved notable success for aspect-based sentiment analysis (ABSA), deep neural networks are susceptible to spurious correlations between input features and output labels, leading to poor robustness. In this paper, we propose a novel Counterfactual-Enhanced Information Bottleneck framework (called CEIB) to reduce spurious correlations for ABSA. CEIB extends the information bottleneck (IB) principle to a factual-counterfactual balancing setting by integrating augmented counterfactual data, with the goal of learning a robust ABSA model. Concretely, we first devise a multi-pattern prompting method, which utilizes the large language model (LLM) to generate high-quality counterfactual samples from the original samples. Then, we employ the information bottleneck principle and separate the mutual information into factual and counterfactual parts. In this way, we can learn effective and robust representations for the ABSA task by balancing the predictive information of these two parts. Extensive experiments on five benchmark ABSA datasets show that our CEIB approach achieves superior prediction performance and robustness over the state-of-the-art baselines. Code and data to reproduce the results in this paper is available at: https://github.com/shesshan/CEIB.

In this paper, we propose a Meta Cooperative Learning (MCL) framework for task-oriented dialog systems (TDSs). Our model consists of an auxiliary KB reasoning task for learning meta KB knowledge, an auxiliary dialogue reasoning task for learning dialogue patterns, and a TDS task (primary task) that aims at not only retrieving accurate entities from KB but also generating natural responses, which are coordinated to achieve collective success in both retrieving accurate KB entities and generating human-like responses via meta learning. Concretely, the dialog generation model amalgamates complementary meta KB and dialog knowledge from two novel auxiliary reasoning tasks that together provide integrated guidance to build a high-quality TDS by adding regularization terms to force primary network to produce similar results to auxiliary networks. While MCL automatically learns appropriate labels for the two auxiliary reasoning tasks from the primary task, without requiring access to any further data. The key idea behind MCL is to use the performance of the primary task, which is trained alongside the auxiliary tasks in one iteration, to improve the auxiliary labels for the next iteration with meta learning. Experimental results on three benchmark datasets show that MCL can generate higher quality responses compared to several strong baselines in terms of both automatic and human evaluations. Code to reproduce the results in this paper is available at: https://github.com/siat-nlp/MCL.

Relation extraction is an important NLP task to extract the semantic relationship between two entities. Recently, large-scale pre-training language models have achieved excellent performance in many NLP applications. Most of the existing relation extraction models mainly rely on context information, but entity information is also very important for relation extraction, especially domain knowledge of entity and the direction between entity pairs. In this paper, based on the pre-trained BERT model, we propose a multi-task joint relation extraction model incorporating knowledge representation learning(KRL). The experimental results on the SemEval 2010 task 8 dataset and the KBP37 dataset show that our proposed model outperforms most of state-of-the-art methods. The results on the larger dataset FewRel80 refined from FewRel also indicate that increasing the knowledge representation learning as an auxiliary objective is helpful for the relation extraction task.

BACKGROUND: Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators. CONCLUSIONS: The experiments on the benchmark datasets from DREAM challenge as well as the E.coli datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).

The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picture-level JND (PJND) of compressed stereo images. The study, which involves 48 subjects and 10 stereoscopic images compressed with H.265 intra coding and JPEG2000, includes two parts. In the first part, we determine the minimum distortion that the HVS can perceive against a pristine stereo image. In the second part, we explore the minimum distortion that each subject perceives against a distorted stereo image. Modeling the distribution of the PJND samples as Gaussian, we obtain their complementary cumulative distribution functions, which are known as Satisfied User Ratio (SUR) functions. Statistical analysis results demonstrate that the SUR is highly dependent on the image contents. The HVS is more sensitive to distortion in images with more texture details. The compressed stereoscopic images and the PJND samples are collected in a data set called SIAT-JSSI, which we release to the public.

The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.

Drug Drug Interactions (DDIs) can cause harmful effect. Two shared tasks, DDIExtraction 2011 and DDIExtraction 2013, have been held to promote the implementation and comparative assessment of natural language processing techniques in the field of the pharmacovigilance domain. However, few model can meanwhile achieve state-of-the-art performance on both tasks. A major reason is the lack of representation of DDI instance structure in common. Therefore, in this paper, we propose a novel method to make full use of the DDI structure based on deep learning, in which we grasp the skeleton structure of DDI instances by a skeleton long short term memory (skeleton-LSTM) network. The experimental results show that our method can achieve an F-score of 0.677 on DDIExtraction 2011 and an F-score of 0.714 on DDIExtraction 2013, both of which are state-of-the-art.

Cluster validation, which is the process of evaluating the quality of clustering results, plays an important role for practical machine learning systems. Categorical sequences, such as biological sequences in computational biology, have become common in real-world applications. Different from previous studies, which mainly focused on attribute-value data, in this paper, we work on the cluster validation problem for categorical sequences. The evaluation of sequences clustering is currently difficult due to the lack of an internal validation criterion defined with regard to the structural features hidden in sequences. To solve this problem, in this paper, a novel cluster validity index (CVI) is proposed as a function of clustering, with the intracluster structural compactness and intercluster structural separation linearly combined to measure the quality of sequence clusters. A partition-based algorithm for robust clustering of categorical sequences is also proposed, which provides the new measure with high-quality clustering results by the deterministic initialization and the elimination of noise clusters using an information theoretic method. The new clustering algorithm and the CVI are then assembled within the common model selection procedure to determine the number of clusters in categorical sequence sets. A case study on commonly used protein sequences and the experimental results on some real-world sequence sets from different domains are given to demonstrate the performance of the proposed method.

BACKGROUND: Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods. CONCLUSIONS: Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.

Privacy is an important concern in the society, and it has been a fundamental issue when to analyze and publish data involving human individual's sensitive information. Recently, the slicing method has been popularly used for privacy preservation in data publishing, because of its potential for preserving more data utility than others such as the generalization and bucketization approaches. However, in this paper, we show that the slicing method has disclosure risks for some absolute facts, which would help the adversary to find invalid records in the sliced microdata table, resulting in breach of individual privacy. To increase the privacy of published data in the sliced tables, a new method called value swapping is proposed in this work, aimed at decreasing the attribute disclosure risk for the absolute facts and ensuring the l-diverse slicing. By value swapping, the published table contains no invalid information such that the adversary cannot breach the individual privacy. Experimental results also show that the NEW method is able to keep more data utility than the existing slicing methods in a published microdata table. Copyright © 2016 John Wiley & Sons, Ltd.

A huge increase in the number of mobile malware brings a serious threat to Internet security, as the adoption rate of mobile device is soaring, especially Android device. A variety of researches have been developed to defense malware, but the mobile device users continuously suffer private information leak or economic losses from malware. Recently, a large number of methods have been proposed based on static or dynamic features analysis combining with machine learning methods, which are considered effective to detect malware on mobile device. In this paper, we propose an effective framework to detect malware on Android device based on feature extraction and neural network calssifier. In this framework, we take use of static features to represent malware and utilize extreme learning machine (ELM) algorithm to learn the neural network. We first extract features from the malware, and then utilize three different feature extraction methods including principal component analysis (PCA), Karhunen-Loève transform (KLT) and independent component analysis (ICA) to transform the feature matrix into new feature spaces and generate three new feature matrixes. For each feature matrix, we construct En base classifiers by using ELM. Finally, we utilize Stacking method to combine the results. Experimental results suggest that the proposed framework is effective in detecting malware on Android device.

In this paper, we propose two stream-based active learning algorithms for single-hidden layer feed-forward neural networks (SLFNs) trained by extreme learning machine (ELM). Uncertainty and inconsistency are adopted as two sample selection criteria. Uncertainty reflects the nondeterminacy of a sample among different decision classes, which is calculated by information entropy or Gini-index. Inconsistency reflects the disagreement of the sample between its conditional features and decision labels, which is calculated by the lower approximations in fuzzy rough sets. Experimental results demonstrate that inconsistency-based strategy is more effective than uncertainty based strategy for SLFNs under stream-based environment.

A key issue in decision tree (DT) induction with continuous valued attributes is to design an effective strategy for splitting nodes. The traditional approach to solving this problem is adopting the candidate cut point (CCP) with the highest discriminative ability, which is evaluated by some frequency based heuristic measures. However, such methods ignore the class permutation of examples in the node, and they cannot distinguish the CCPs with the same or similar frequency information, thus may fail to induce a better and smaller tree. In this paper, a new concept, i.e., segment of examples, is proposed to differentiate the CCPs with same frequency information. Then, a new hybrid scheme that combines the two heuristic measures, i.e., frequency and segment, is developed for splitting DT nodes. The relationship between frequency and the expected number of segments, which is regarded as a random variable, is also given. Experimental comparisons demonstrate that the proposed scheme is not only effective to improve the generalization capability, but also valid to reduce the size of the tree.

Clustering categorical sequences is an important and difficult data mining task. Despite recent efforts, the challenge remains, due to the lack of an inherently meaningful measure of pairwise similarity. In this paper, we propose a novel variable-order Markov framework, named weighted conditional probability distribution (WCPD), to model clusters of categorical sequences. We propose an efficient and effective approach to solve the challenging problem of model initialization. To initialize the WCPD model, we propose to use a first-order Markov model built on a weighted fuzzy indicator vector representation of categorical sequences, which we call the WFI Markov model. Based on a cascade optimization framework that combines the WCPD and WFI models, we design a new divisive hierarchical clustering algorithm for clustering categorical sequences. Experimental results on data sets from three different domains demonstrate the promising performance of our models and clustering algorithm.

BACKGROUND: Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors. RESULTS: The proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust, CD-HIT-EST and some others. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences. CONCLUSIONS: We introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.

Clustering high-dimensional data is a major challenge due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension to traditional clustering that attempts to find projected clusters in subsets of the dimensions of a data space. In this paper, a probability model is first proposed to describe projected clusters in high-dimensional data space. Then, we present a model-based algorithm for fuzzy projective clustering that discovers clusters with overlapping boundaries in various projected subspaces. The suitability of the proposal is demonstrated in an empirical study done with synthetic data set and some widely used real-world data set.

Clustering categorical sequences remains an open and challenging task due to the lack of an inherently meaningful measure of pair wise similarity between sequences. Model initialization is an unsolved problem in model-based clustering algorithms for categorical sequences. In this paper, we propose a simple and effective Markov model to approximate the conditional probability distribution (CPD) model, and use it to design a novel two-tier Markov model to represent a sequence cluster. Furthermore, we design a novel divisive hierarchical algorithm for clustering categorical sequences based on the two-tier Markov model. The experimental results on the data sets from three different domains demonstrate the promising performance of our models and clustering algorithm.

In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.

Malware is software designed to infiltrate or damage a computer system without the owner's informed consent (e.g., viruses, backdoors, spyware, trojans, and worms). Nowadays, numerous attacks made by the malware pose a major security threat to computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed, named "gray list," on a daily basis is constantly increasing. In order to help our virus analysts, quickly and efficiently pick out the malicious executables from the "gray list," an automatic and robust tool to analyze and classify the file samples is needed. In our previous work, we have developed an intelligent malware detection system (IMDS) by adopting associative classification method based on the analysis of application programming interface (API) execution calls. Despite its good performance in malware detection, IMDS still faces the following two challenges: (1) handling the large set of the generated rules to build the classifier; and (2) finding effective rules for classifying new file samples. In this paper, we first systematically evaluate the effects of the postprocessing techniques (e.g., rule pruning, rule ranking, and rule selection) of associative classification in malware detection, and then, propose an effective way, i.e., CIDCPF, to detect the malware from the "gray list." To the best of our knowledge, this is the first effort on using postprocessing techniques of associative classification in malware detection. CIDCPF adapts the postprocessing techniques as follows: first applying Chi-square testing and Insignificant rule pruning followed by using Database coverage based on the Chi-square measure rule ranking mechanism and Pessimistic error estimation, and finally performing prediction by selecting the best First rule. We have incorporated the CIDCPF method into our existing IMDS system, and we call the new system as CIMDS system. Case studies are performed on the large collection of file samples obtained from the Antivirus Laboratory at Kingsoft Corporation and promising experimental results demonstrate that the efficiency and ability of detecting malware from the "gray list" of our CIMDS system outperform popular antivirus software tools, such as McAfee VirusScan and Norton Antivirus, as well as previous data-mining-based detection systems, which employed Naive Bayes, support vector machine, and decision tree techniques. In particular, our CIMDS system can greatly reduce the number of generated rules, which makes it easy for our virus analysts to identify the useful ones.

Currently, the most significant line of defense against malware is anti-virus products which focus on authenticating valid software from a white list, blocking invalid software from a black list, and running any unknown software (i.e., the gray list) in a controlled manner. The gray list, containing unknown software programs which could be either normal or malicious, is usually authenticated or rejected manually by virus analysts. Unfortunately, along with the development of the malware writing techniques, the number of file samples in the gray list that need to be analyzed by virus analysts on a daily basis is constantly increasing. In this paper, we develop an intelligent file scoring system (IFSS for short) for malware detection from the gray list by an ensemble of heterogeneous base-level classifiers derived by different learning methods, using different feature representations on dynamic training sets. To the best of our knowledge, this is the first work of applying such ensemble methods for malware detection. IFSS makes it practical for virus analysts to identify malware samples from the huge gray list and improves the detection ability of anti-virus software. It has already been incorporated into the scanning tool of Kingsoft's Anti-Virus software. The case studies on large and real daily collection of the gray list illustrate that the detection ability and efficiency of our IFSS system outperforms other popular scanning tools such as NOD32 and Kaspersky.

In recent years, a large number of identity-based key agreement protocols from pairings have been proposed. Some of them are elegant and practical. However, using most of those protocols, the session key which users arrive is depended on the identity information. In this paper, we first propose a two-party identity-based key agreement protocol based on pairings whose security relies on the hardness of the bilinear Diffie-Hellman problem in the random oracle model, and the session key is independent of the identity information. After that we extend the protocol to an identity-based group key agreement protocol, which uses no broadcasting. The proposed key agreement protocol achieves the security attributes: known session key security, perfect forward secrecy, no key compromise impersonation, no unknown key share and no key control.

Key agreement protocols allow two or more participants exchange information over an open network with each other. In modern collaborative and distributed applications, authenticated group key agreement is one of the important issues. Recently identity-based authenticated key agreement protocol has been increasingly investigated because of the simplicity of a public key management. In this paper, we propose an authenticated three-round identity-based group key agreement protocol. The first round is for identity authentication, the second round is for key agreement, and the third round is for key confirmation. The proposed protocol is based on the protocol of Burmester and Desmedt, and the protocol is provably- secure in the random oracle model. In particular, the proposed authenticated identity-based group key agreement protocol provides a batch verification technique, which verifies the validity of transcripts from other group players simultaneously.

Clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. In this paper, the problem of modeling projected clusters is first discussed, and an extended Gaussian model is proposed. Second, a general objective criterion used with k-means type projective clustering is presented based on the model. Finally, the expressions to learn model parameters are derived and then used in a new algorithm named FPC to perform fuzzy clustering on high dimensional data. The experimental results on document clustering show the effectiveness of the proposed clustering model.

An automatic detection model for ship targets in RADARSAT SAR images is being developed by using statistical methods, Radon transform and other image processing techniques. This paper presents current progress made on the detection model.